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Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests.

Understanding the population dynamics of migratory animals throughout the full annual cycle is critical for effective conservation, and requires knowing where populations are located throughout the year. For most Neotropical-Nearctic migratory bird species, breeding distributions are well-described, but less is generally known about their migratory paths, stopover areas, and wintering grounds. The Kirtland’s warbler Setophaga kirtlandii is an endangered long-distance migratory passerine with a restricted but well-described breeding distribution located primarily in Michigan (USA), with small subpopulations in Wisconsin (USA) and Ontario (Canada). Kirtland’s warbler migratory paths and the location of important stopover areas were recently estimated, but published accounts of their wintering distribution and winter habitat use are currently incomplete and conflicting. In this paper, we used 128 detections from playback surveys, 86 sightings from birders entered into eBird, and previously published light-level geolocator tracking data from 27 males to refine the distribution and habitat use of wintering Kirtland’s warblers. We demonstrate that Kirtland’s warblers are most abundant in the central Bahamas, but also winter elsewhere in The Bahamas, Turks and Caicos, Cuba, and possibly southern Florida and Bermuda. Despite some previous accounts, which suggest that Kirtland’s warblers primarily winter in Caribbean pine habitat, our survey and eBird data indicate that they almost exclusively use a variety of broadleaf scrub habitats. This new information will help guide future research and conservation efforts designed to protect this endangered species.

Clouds and cloud shadows block land surface information in optical satellite images. Accurate detection of clouds and cloud shadows can help exclude these contaminated pixels in further applications. Existing cloud screening methods are challenged by cloudy regions where most of satellite images are contaminated by clouds. To solve this problem for landscapes where the typical frequency of cloud-free observations of a pixel is too small to use existing methods to mask clouds and shadows, this study presents a new Automatic Time-Series Analysis (ATSA) method to screen clouds and cloud shadows in multi-temporal optical images. ATSA has five main steps: (1) calculate cloud and shadow indices to highlight cloud and cloud shadow information; (2) obtain initial cloud mask by unsupervised classifiers; (3) refine initial cloud mask by analyzing time series of a cloud index; (4) predict the potential shadow mask using geometric relationships; and (5) refine the potential shadow mask by analyzing time series of a shadow index. Compared with existing methods, ATSA needs fewer predefined parameters, does not require a thermal infrared band, and is more suitable for areas with persistent clouds. The performance of ATSA was tested with Landsat-8 OLI images, Landsat-4 MSS images, and Sentinel-2 images in three sites. The results were compared with a popular method, Function of Mask (Fmask), which has been adopted by USGS to produce Landsat cloud masks. These tests show that ATSA and Fmask can get comparable cloud and shadow masks in some of the tested images. However, ATSA can consistently obtain high accuracy in all images, while Fmask has large omission or commission errors in some images. The quantitative accuracy was assessed using manual cloud masks of 15 images. The average cloud producer's accuracy of these 15 images is as high as 0.959 and the average shadow producer's accuracy reaches 0.901. Given that it can be applied to old satellite sensors and it is capable for cloudy regions, ATSA is a valuable supplement to the existing cloud screening methods.

Many studies have investigated the impacts of typhoons on natural vegetation, but the influencing factor of urban vegetation damage from super typhoon is not clear. Therefore, this study investigated the vegetation damage patterns in eight cities affected by Typhoon Mangkhut (the 2nd strongest tropical storm worldwide in 2018) using the normalized difference vegetation index (NDVI) derived from Sentinel-2 images. The vegetation damage maps have an overall accuracy of 97% using the very high-resolution WorldView-3 images as reference data. The results show that (1) The typhoon-induced vegetation damage show high spatial heterogeneity in urban areas and varies with land cover types. Residential greenspace and street trees are more susceptible to typhoon disturbance than natural vegetation. (2) Wind intensity is still an important factor in urban vegetation damage (r2 = 0.43, P value

Studies of land surface dynamics in heterogeneous landscapes often require remote sensing data with high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial resolution images through blending two types of data, i.e., frequent coarse spatial resolution data, such as that from MODIS, and less frequent high spatial resolution data such as that from Landsat. The proposed method is based on spectral unmixing analysis and a thin plate spline interpolator. Compared with existing spatiotemporal data fusion methods, it has the following strengths: (1) it needs minimum input data; (2) it is suitable for heterogeneous landscapes; and (3) it can predict both gradual change and land cover type change. Simulated data and real satellite images were used to test the performance of the proposed method. Its performance was compared with two very popular methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an unmixing-based data fusion (UBDF) method. Results show that the new method creates more accurate fused images and keeps more spatial detail than STARFM and UBDF. More importantly, it closely captures reflectance changes caused by land cover conversions, which is a big issue with current spatiotemporal data fusion methods. Because the proposed method uses simple principles and needs only one fine-resolution image as input, it has the potential to increase the availability of high-resolution time-series data that can support studies of rapid land surface dynamics.

Because the world's forests play a major role in regulating nutrient and carbon cycles, there is much interest in estimating their biomass. Estimates of aboveground biomass based on well-established methods are relatively abundant; estimates of root biomass based on standard methods are much less common. The goal of this work was to determine if a reliable method to estimate root biomass density for forests could be developed based on existing data from the literature. The forestry literature containing root biomass measurements was reviewed and summarized and relationships between both root biomass density (Mg ha−1) and root:shoot ratios (R/S) as dependent variables and various edaphic and climatic independent variables, singly and in combination, were statistically tested. None of the tested independent variables of aboveground biomass density, latitude, temperature, precipitation, temperature:precipitation ratios, tree type, soil texture, and age had important explanatory value for R/S. However, linear regression analysis showed that aboveground biomass density, age, and latitudinal category were the most important predictors of root biomass density, and together explained 84% of the variation. A comparison of root biomass density estimates based on our equations with those based on use of generalized R/S ratios for forests in the United States indicated that our method tended to produce estimates that were about 20% higher.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, longterm trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Seasonally dry tropical forests are distributed across Latin America and the Caribbean and are highly threatened, with less than 10% of their original extent remaining in many countries. Using 835 inventories covering 4660 species of woody plants, we show marked floristic turnover among inventories and regions, which may be higher than in other neotropical biomes, such as savanna. Such high floristic turnover indicates that numerous conservation areas across many countries will be needed to protect the full diversity of tropical dry forests. Our results provide a scientific framework within which national decision-makers can contextualize the floristic significance of their dry forest at a regional and continental scale., This paper is the result of the Latin American and Caribbean Seasonally Dry Tropical Forest Floristic Network (DRYFLOR), which has been supported at the Royal Botanic Garden Edinburgh by a Leverhulme Trust International Network Grant (IN-074). This work was also supported by the U.K. Natural Environment Research Council grant NE/ I028122/1; Colciencias Ph.D. scholarship 529; Synthesys Programme GBTAF-2824; the NSF (NSF 1118340 and 1118369); the Instituto Humboldt (IAvH)–Red colombiana de investigación y monitoreo en bosque seco; the Inter-American Institute for Global Change Research (IAI; Tropi-Dry, CRN2-021, funded by NSF GEO 0452325); Universidad Nacional de Rosario (UNR); and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). The data reported in this paper are available at www.dryflor.info. R.T.P. conceived the study. M.P., A.O.-F., K.B.-R., R.T.P., and J.W. designed the DRYFLOR database system. K.B.-R. and K.G.D. carried out most analyses. K.B.-R. R.T.P., and K.G.D. wrote the manuscript with substantial input from A.D.-S., R.L.-P., A.O.-F., D.P., C.Q., and R.R.

Tropical forest managers need detailed maps of forest types for REDD+, but spectral similarity among forest types; cloud and scan-line gaps; and scarce vegetation ground plots make producing such maps with satellite imagery difficult. How can managers map tropical forest tree communities with satellite imagery given these challenges? Here we describe a case study of mapping tropical forests to floristic classes with gap-filled Landsat imagery by judicious combination of field and remote sensing work. For managers, we include background on current and forthcoming solutions to the problems of mapping detailed tropical forest types with Landsat imagery. In the study area, Trinidad and Tobago, class characteristics like deciduousness allowed discrimination of floristic classes. We also discovered that we could identify most of the tree communities in (1) imagery with fine spatial resolution of ⩽1 m; (2) multiseason fine resolution imagery (viewable with Google Earth); or (3) Landsat imagery from different dates, particularly imagery from drought years, even if decades old, allowing us to collect the extensive training data needed for mapping tropical forest types with “noisy” gap-filled imagery. Further, we show that gap-filled, synthetic multiseason Landsat imagery significantly improves class-specific accuracy for several seasonal forest associations. The class-specific improvements were better for comparing classification results; for in some cases increases in overall accuracy were small. These detailed mapping efforts can lead to new views of tropical forest landscapes. Here we learned that the xerophytic rain forest of Tobago is closely associated with ultramafic geology, helping to explain its unique physiognomy.

Different disturbances in similar habitats can produce unique successional assemblages of plants. We collected plant species composition and cover data to investigate the effects of three common types of disturbances—fire, anthropogenic clearing (‘cleared’), and clearing followed by goat grazing (‘cleared‐and‐grazed’)—on early‐successional coppice (dry forest) community structure and development on Eleuthera, Bahamas. For each disturbance type, both the ground layer ( 0.5 m height) were sampled in eight patches ( > 1 ha) of varying age (1–28 yr) since large‐scale mature coppice disturbance. Overall, plant communities differed among disturbance types; several common species had significantly higher cover in the shrub layer of fire patches, and cleared‐ and‐grazed patches exhibited higher woody ground cover. Total percent cover in the shrub layer increased in a similar linear fashion along the investigated chronosequence of each disturbance type; however, cover of the common tree species, Bursera simaruba, increased at a notably slower rate in cleared‐and‐grazed patches. The pattern of increase and subsequent decrease in cover of Lantana spp. and Zanthoxylum fagara in the shrub layer was characterized by longer persistence and higher covers, respectively, in cleared‐and‐grazed patches, which also exhibited low peak cover and fast decline of nonwoody ground cover. Our results suggest that goats may accelerate some aspects of succession (e.g., quickly removing nonwoody ground cover) and retard other aspects (e.g., inhibiting growth of tree species and maintaining early‐successional shrubs in the shrub layer). These effects may lead to different successional trajectories, and have important conservation implications.

We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.

Remote sensing of forest vertical structure is possible with lidar data, but lidar is not widely available. Here we map tropical dry forest height (RMSE = 0.9 m, R2 = 0.84, range 0.6–7 m), and we map foliage height profiles, with a time series of Landsat and Advanced Land Imager (ALI) imagery on the island of Eleuthera, The Bahamas, substituting time for vertical canopy space. We also simultaneously map forest disturbance type and age. We map these variables in the context of avian habitat studies, particularly for wintering habitat of an endangered Nearctic-Neotropical migrant bird, the Kirtland's Warbler (Dendroica kirtlandii). We also illustrate relationships between forest vertical structure, disturbance type and counts of forage species important to the Kirtland's Warbler. The ALI imagery and the Landsat time series are both critical to the result for forest height, which the strong relationship of forest height with disturbance type and age facilitates. Also unique to this study is that seven of the eight image time steps are cloud-cleared images: mosaics of the clear parts of several cloudy scenes. We created each cloud-cleared image, including a virtually seamless ALI image mosaic, with regression tree normalization. We also illustrate how viewing time series imagery as red–green–blue composites of tasseled cap wetness (RGB wetness composites) aids reference data collection for classifying tropical forest disturbance type and age. Our results strongly support current Landsat Program production of co-registered imagery, and they emphasize the value of seamless time series of cloud-cleared imagery.

To characterize the nonbreeding habitat of Kirtland's Warbler (Dendroica kirtlandii) on Eleuthera, The Bahamas, we quantified the habitat at sites where we captured the warblers and compared these traits with those of random sites and sites of tall coppice. On the basis of a chronosequence of satellite imagery, 153 capture sites ranged in age from 3 to 28 years after human disturbance, mean 14.6 years ± 6.3 (SD). Capture sites had been abandoned after clearing (65%), converted to goat pasture (26%), burned (2%), or were young second growth following unknown disturbance (7%). Canopies in 104 capture plots were lower (mean 1.8 m) than canopies in random plots (mean 2.7 m) and plots of late-successional tall coppice (mean 6.3 m). At seven sites mean foliage density in capture plots was consistently greatest at 0.5 to 1.0 m height, but the sites were heterogeneous for other foliage-height classes

Environmental and past land use controls on tree species assemblages on the Commonwealth of Puerto Rico and the U.S. Virgin Islands were characterized to determine whether biophysical factors or land-use history has been more important in determining the species composition of secondary tropical forests after large-scale forest clearing for agriculture, widespread species introduction, and landscape-scale forest fragmentation. Post-deforestation, secondary forest assemblages are comprehensively described, both as broad general assemblages and island-specific variations by calculating species importance values from forest inventory data. Hierarchical clustering and indicator species analysis defined species assemblages, and then correlations between species assemblages and environmental variables were explored with non-metric multidimensional scaling, analysis of variance and χ 2 testing. These assemblages are arrayed along environmental gradients of decreasing spring moisture stress, decreasing maximum temperatures, and increasing minimum temperatures. Land-use history is not as important to determining variation in species composition across climatic zones, although several species assemblages are associated with certain geology types or land-use histories. Naturalized tree species are prominent in these secondary forests and contribute to the formation of some novel assemblages, but native late and early successional species also colonize former agricultural land, all influenced by the degree of disturbance. We conclude that environmental factors have an overarching effect on forest species composition across the broader range of climatic, geologic and topographic conditions and larger geographic scales, while land-use history influences subtropical secondary forest species assemblages within a specific climatic zone or set of relatively narrow environmental conditions.

Classification-trees were used to model forest type groups and forest types for the conterminous United States and Alaska. The predictor data were a geospatial data set with a spatial resolution of 250 m developed by the U.S. Department of Agriculture Forest Service (USFS). The response data were plot data from the USFS Forest Inventory and Analysis program. Overall accuracies for the conterminous U.S. for the forest type group and forest type were 69 percent (Kappa � 0.66) and 50 percent (Kappa � 0.57), respectively. The overall accuracies for Alaska for the forest type group and forest type were 78 percent (Kappa � 0.69) and 67 percent (Kappa � 0.61), respectively. This is the first forest type map produced for the U.S. The forest type group map is an update of a previous forest type group map created by Zhu and Evans (1994).

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud...

On the Caribbean island of Puerto Rico, forest, urban/built-up, and pasture lands have replaced most formerly cultivated lands. The extent and age distribution of each forest type that undergoes la...

Cloud-free optical satellite imagery simplifies remote sensing, but land-cover phenology limits existing solutions to persistent cloudiness to compositing temporally resolute, spatially coarser imagery. Here, a new strategy for developing cloud-free imagery at finer resolution permits simple automatic change detection. The strategy uses regression trees to predict pixel values underneath clouds and cloud shadows in reference scenes from other scene dates. It then applies improved histogram matching to adjacent scenes. In the study area, the islands of Puerto Rico, Vieques, and Culebra, Landsat image mosaics resulting from this strategy permit accurate detection of land development with only spectral data and maximum likelihood classification. Between about 1991 and 2000, urban/built-up lands increased by 7.2 percent in Puerto Rico and 49 percent in Vieques and Culebra. The regression tree modeling and histogram matching require no manual interpretation. Consequently, they can support large volume processing to distribute cloud-free imagery for simple change detections with common classifiers.

The species composition of forests change continuously as the earth’s biota evolves and adjusts to environmental change. Humans are accelerating the rate of species turnover by moving species around the planet and dramatically changing environmental conditions. Our focus is on new forests in Puerto Rico that emerge naturally on abandoned lands previously converted to agriculture and degraded. These forest stands have combinations of species that are new to the island’s landscapes. New forests exhibit high species dominance during forest establishment, which includes dominance by alien tree species. These alien tree species establish and maintain forest cover, which may facilitate regeneration of native tree species. Landscape analysis and literature review revealed that these emerging stands are highly fragmented (60% were

In the Caribbean island of Puerto Rico, rapid land-use changes over the past century have included recent land-cover conversion to urban/built-up lands. Observations of this land development adjacent to reserves or replacing dense forest call into question how the changes relate to forests or reserved lands. Using existing maps, this study first summarizes island-wide land-cover change between 1977-78 and 1991-92. Then, using binomial logit modeling, it seeks evidence that simple forest cover attributes, reserve locations, or existing land cover influence land development locations. Finally, this study quantifies land development, reserve protection and forest cover by ecological zone. Results indicate that 1) pasture is more likely to undergo land development than shrubland plus forest with low canopy density, 2) forest condition and conservation status appear unimportant in that development locations neither distinguish between classes of forest canopy development nor relate to forest patch size or reserve proximity, and 3) most land development occurs in the least-protected ecological zones. Outside the boundaries of strictly protected forest and other reserves, accessibility, proximity to existing urban areas, and perhaps desirable natural settings, serve to increase land development. Over the coming century, opportunities to address ecological zone gaps in the island’s forest reserve system could be lost more rapidly in lowland ecological zones, which are relatively unprotected.

Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regression based Parameter-elevation Regressions on Independent Slopes Model (PRISM) uses spatial data sets, a knowledge base and expert interaction to generate GIS-compatible grids of climate variables. This study applied PRISM to generate maps of mean monthly and annual precipitation and minimum and maximum temperature for the Caribbean islands of Puerto Rico, Vieques and Culebra over the 1963-1995 averaging period. PRISM was run under alternative parameterizations that simulated simpler interpolation methods as well as the full PRISM model. For temperature, the standard PRISM parameterization was compared to a hypsometric method, in which the temperature/elevation slope was assumed to be -6.5°C/km (HYPS). For precipitation, the standard PRISM parameterization was compared to an inverse-distance weighting interpolation (IDW). Spatial temperature patterns were linked closely to elevation, topographic position, and coastal proximity. Both PRISM and HYPS performed well for July maximum temperature, but HYPS performed relatively poorly for January minimum temperature, due primarily to lack of a spatially varying temperature/elevation slope, vertical atmospheric layer definition, and coastal proximity guidance. Mean monthly precipitation varied significantly throughout the year, reflecting seasonally differing moisture trajectories. Spatial precipitation patterns were associated most strongly with elevation, upslope exposure to predominant moisture-bearing winds, and proximity to the ocean. IDW performed poorly compared to PRISM, due largely to the lack of elevation and moisture availability information. Overall, the full PRISM approach resulted in greatly improved performance over simpler methods for precipitation and January minimum temperature, but only a small improvement for July maximum temperature. Comparisons of PRISM mean annual temperature and precipitation maps to previously-published, hand-drawn maps showed similar overall patterns and magnitudes, but the PRISM maps provided much more spatial detail.

Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots

When mapping land cover with satellite imagery in montane tropical regions, varying illumination angles and ecological zones can obscure the differences between spectral responses of old-growth forest, secondary forest and agricultural lands. We used multi-date, Landsat Thematic Mapper (TM) imagery to map secondary forests, agricultural lands and old-growth forests in the Talamanca Mountain Range in southern Costa Rica. With stratification by illumination and ecological zone, the overall accuracy for this classification was 87% with a Kappa coefficient of 0.83. We also examined spectral responses to forest successional stage, ecological zone and aspect illumination for the TM Tasselled Cap indices, TM (2 x 6)/7, TM 4/5 and TM difference bands, and whether using digital data from multiple decades improved classification accuracy. Digital maps of ecological zones should be useful for large-scale mapping of land use and forest successional stage in complex montane regions such as those in Central America.

Multinomial logistic models of land use/land cover in montane Costa Rica and landscape pattern analysis showed that relative to agriculture, secondary forest occurred closer to old-growth forest, further from roads, in forest reserves, and at higher elevations. Collinearity between explanatory variables yielded simple multivariate models; proportion of surrounding old growth predicted secondary forest most accurately. An old-growth matrix [mean patch size (MPS) 24.5 ha], located mainly within protected areas, dominated elevations greater than 2500 m. A matrix of agriculture (MPS 23.5 ha), with smaller patches (approximately 9 ha) of secondary forest and old growth, dominated elevations from 1500 to 2500 m. Combining secondary forest with old growth decreased forest patch number and increased MPS from 7.3 to 37.1 ha. I concluded that: (a) secondary forest pattern is nonrandom, so ancillary data will aid its mapping with satellite imagery. The variables elevation, agriculture distance, road distance, and population density distinguished secondary forest from old growth with 74% accuracy; (b) socioeconomic and biological forces probably interact to create these secondary forest patterns; and (c) the strong association between secondary forest and old growth supports the concept that tropical forest recovery depends on the landscape structure of remnant forest.

Interactions between road and stream networks are complex and are influenced by a range of environmental and road design characteristics. These interactions are not clearly understood and are the subjects of current research. To increase understanding of these interactions we explore the concepts of Road and Stream Network Connectivity (R/S Connectivity) and Road and Stream Network Connectivity Potential (RSNCP). Lastly we provide a methodology for study and analysis of R/S connectivity. This study focuses on road induced alterations to sediment and water flow processes, which are important road effects of R/S connectivity. For 25 river road crossings (RRC) in the Rio Mameyes and Rio Espiritu watersheds of Northeastern Puerto Rico, a multi-scale Geographic Information Systems (GIS) database measuring environmental and road characteristic variables was developed specifically to measure variables influencing sediment and water flow. Multivariate analysis methods were used to select the environmental and road characteristic variables which were used in multiple linear regression models for three biota variables (Decapod Richness, Adult Fish Richness, and Total Richness), and four stream habitat geomorphology variables (Median Channel Grain Size, Active Channel Maximum Depth, Pool Volume, and Active Channel Width). Explained variance (R2) from modeling results ranged from 0.22 to 0.86, demonstrating that the GIS derived variables can successfully be used to model important stream biota and geomorphology response variables.

[1] Little is known about the tropical forests that undergo clearing as urban/built-up and other developed lands spread. This study uses remote sensing-based maps of Puerto Rico, multinomial logit models and forest inventory data to explain patterns of forest age and the age of forests cleared for land development and assess their implications for forest carbon storage and tree species richness. Accessibility, arability and spatial contagion emerge strongly as overriding spatial controls on tropical forest age, determining (1) the pattern of agricultural abandonment that permits forest regrowth, and (2) where humans leave old-growth forest remnants. Covariation between the factors patterning forest age and land development explains why most forest cleared for land development is younger. Forests are increasingly younger in more accessible and fertile areas where agriculture has lasted longer and land development is most common. All else equal, more species-rich older forest on less arable lands are somewhat less likely to undergo development, but they are still vulnerable to clearing for land development if close to urban centers and unprotected. Accounting for forest age leads to a 19% lower estimate of forest biomass cleared for land development than if forest age is not accounted for.

Aims The fruits of Erithalis fruticosa L. and Lantana involucrata L. are important in the diet of US federally endangered Kirtland’s Warblers (Setophaga kirtlandii) wintering in the Bahamas archipelago. These two shrubs occur in tropical and subtropical dry forests, including forests that have been subjected to recent disturbance. Despite their importance to the endangered warbler, the disturbance ecology of these shrubs is poorly understood. We sought to determine, based on functional characteristics of the plants, whether their presence is favored by a particular type or regime of disturbance. Methods We used data from field experiments (seed broadcasting and shrub cutting) conducted on the island of Eleuthera, The Bahamas to determine mechanisms of and conditions favoring establishment and persistence (‘vital attributes’) of E. fruticosa and L. involucrata, which enabled categorization according to the plant functional types defined by Noble and Slatyer (1980). We then compared hypothesized distributions of these plant functional types among different anthropogenic disturbance regimes to observed distributions of E. fruticosa and L. involucrata in order to identify distur bance regimes most likely to produce habitat used by Kirtland’s Warblers. Important Findings E. fruticosa and L. involucrata were functionally categorized as widely dispersed but largely shade intolerant species capable of establishing or regenerating individuals after disturbance via both seeds and vegetative mechanisms. Both hypothesized and observed distribution patterns indicated the shrubs were favored by a regime of frequent disturbance producing open canopy and ground layers. Among the anthropogenic disturbances we examined, areas of large-scale land clearing combined with subsequent goat grazing most often supported E. fruticosa and L. involucrata, while the shrubs were relatively rare in burned areas. Utilizing the plant functional type framework in combination with field data to evaluate predictions of species occurrence among different disturbances regimes provides a strong theoretical basis for conservation strategies. Understanding which disturbance types favor a habitat of concern and the mechanisms by which they do so can aid the prior

Litter chemistry varies across landscapes according to factors rarely examined simultaneously. We analyzed 11 elements in forest floor (fallen) leaves and additional litter components from 143 forest inventory plots systematically located across Puerto Rico, a tropical island recovering from large-scale forest clearing. We assessed whether three existing, independently-derived, landscape classifications (Holdridge life zone, remotely sensed forest type (leaf longevity combined with geology generalized to karst vs. non-karst), and plot-based measures of forest assemblage) would separate observed gradients. With principal component and regression analyses, we also tested whether climate-, landscape- (geology, elevation, aspect, percent slope, slope position, distance from coast), and stand-scale (tree species composition, basal area, density, stand age) variables explained variation in fallen leaf chemistry and stoichiometry. For fallen leaves, C, Ca, Mg, Na, and Mn concentrations differed by Holdridge life zone and C, P, Ca, Mn, Al, and Fe concentrations differed by forest type, where leaf longevity distinguished C and Ca concentrations and geology distinguished C, P, Ca, Mn, Al, and Fe concentrations. Fallen leaf C, P, Ca, and Mn concentrations also differed, and N concentrations only differed, by forest assemblage. Across several scales, fallen leaf N concentration was positively related to the basal area of putatively N2-fixing tree legumes, which were concentrated in lower topographic positions, providing for the first time a biological explanation for the high N concentrations of fallen leaves in these locations. Phosphorus concentrations in fallen leaves by forest assemblages also correlated with the basal area of N2-fixing legumes, and P and N concentrations decreased with mean age of assemblage. Fallen leaves from younger (

Publisher Summary Costa Rica is a well-studied country from the perspective of its biota. A prominent feature holds implications for future management of greenhouse gas emissions in Costa Rica and other tropical areas. If clearing of the dry and moist forest formations throughout the world has or will shift to clearing of wetter forests, much greater quantities of trace GHGs will be released. Information gathering should include a thorough evaluation of carbon stores in dead plant materials (litter, organic soil horizons, dead wood). Such information could provide additional insight into an analysis. The high-elevation forests may, in some cases, have thick organic soil horizons and large amounts of downed wood. Therefore, the biomass density remaining there might be even greater relative to other forest types than currently appears to be the case. If there is any hope for sustainability of resources in Costa Rica, the forests should be a good candidate, but the forests are a large source of carbon. A reforestation program could make Costa Rica a carbon sink, but stopping deforestation appears to have a much larger impact. The productivity of moist forests is sufficient to sequester all CO2 released from fossil fuel burning if a very large proportion of the land area of the country was devoted to that.

We estimate the age of humid lowland tropical forests in Rondonia, Brazil, from a somewhat densely spaced time series of Landsat images (1975-2003) with an automated procedure, the Threshold Age Mapping Algorithm (TAMA), first described here. We then estimate a landscape-level rate of aboveground woody biomass accumulation of secondary forest by combining forest age mapping with biomass estimates from the Geoscience Laser Altimeter System (GLAS). Though highly variable, the estimated average biomass accumulation rate of 8.4 Mg ha-1 yr-1 agrees well with ground-based studies for young secondary forests in the region. In isolating the lowland forests, we map land cover and general types of old-growth forests with decision tree classification of Landsat imagery and elevation data. We then estimate aboveground live biomass for seven classes of old-growth forest. TAMA is simple, fast, and self-calibrating. By not using between-date band or index differences or trends, it requires neither image normalization nor atmospheric correction. In addition, it uses an approach to map forest cover for the self-calibrations that is novel to forest mapping with satellite imagery; it maps humid secondary forest that is difficult to distinguish from old-growth forest in single-date imagery; it does not assume that forest age equals time since disturbance; and it incorporates Landsat Multispectral Scanner imagery. Variations on the work that we present here can be applied to other forested landscapes. Applications that use image time series will be helped by the free distribution of coregistered Landsat imagery, which began in December 2008, and of the Ice Cloud and land Elevation Satellite Vegetation Product, which simplifies the use of GLAS data. Finally, we demonstrate here for the first time how the optical imagery of fine spatial resolution that is viewable on Google Earth provides a new source of reference data for remote sensing applications related to land cover.

Current information on land cover, forest type and forest structure for the Virgin Islands is critical to land managers and researchers for accurate forest inventory and ecological monitoring. In this study, we use cloud free image mosaics of panchromatic sharpened Landsat ETM+ images and decision tree classification software to map land cover and forest type for the Virgin Islands, illustrating a low cost, repeatable mapping approach. Also, we test if coarse-resolution discrete lidar data that are often collected in conjunction with digital orthophotos are useful for mapping forest structural attributes. This approach addresses the factors that affect vegetation distribution and structure by testing if environmental variables can improve regression models of forest height and biomass derived from lidar data. The overall accuracy of the 29 forest and non-forest classes is 72%, while most the forest types are classified with greater than 70% accuracy. Due to the large point spacing of this lidar dataset, it is most appropriate for height measurements of dominant and co-dominant trees (R2 = 72%) due to its inability to accurately represent forest understory. Above ground biomass per hectare is estimated by its direct relationship with plot canopy height (R2 = 0.72%).

[Figure][1] Free image. This Landsat 5 image of the southeastern corner of the Black Sea is part of the general U.S. archive that will be accessible for free under the new USGS policy. CREDIT: BOSTON UNIVERSITY CENTER FOR REMOTE SENSING We are entering a new era in the Landsat Program, the oldest and most venerable of our Earth-observing satellite programs. With little fanfare, the U.S. Geological Survey (USGS) has begun providing imagery for free over the Internet. Throughout the history of the Landsat Program, the cost and access to imagery has always limited our ability to study our planet and the way it is changing. Beginning with a pilot program to provide “Web-enabled” access to Landsat 7 images of the United States that were collected between 2003 and this year, the USGS now plans to provide top-quality image products for free upon request for the entire U.S. archive, including over 2 million images back to Landsat 1 (1972) [for details and schedules, see ([1][2])]. The release by NASA and the USGS in January 2008 of a new Landsat Data Distribution Policy ([2][3]) was a key step to this goal. Free imagery will enable reconstruction of the history of Earth's surface back to 1972, chronicling both anthropogenic and natural changes during a time when our population doubled and the impacts of climate change became noticeable. The Landsat Science Team: 1. 1.[↵][4]USGS Technical Announcement ([http://landsat.usgs.gov/images/squares/USGS\_Landsat\_Imagery_Release.pdf][5]). 2. 2.[↵][6]Landsat Missions ([http://ldcm.usgs.gov/pdf/Landsat\_Data\_Policy.pdf][7]). [1]: pending:yes [2]: #ref-1 [3]: #ref-2 [4]: #xref-ref-1-1 "View reference 1. in text" [5]: http://landsat.usgs.gov/images/squares/USGS_Landsat_Imagery_Release.pdf [6]: #xref-ref-2-1 "View reference 2. in text" [7]: http://ldcm.usgs.gov/pdf/Landsat_Data_Policy.pdf

Contrary to the general trend in the tropics, forests have recovered in Puerto Rico from less than 10% of the landscape in the late 1940s to more than 40% in the present. The recent Puerto Rican history of forest recovery provides the opportunity to study the ecological consequences of economic globalization, reflected in a shift from agriculture to manufacturing and in human migration from rural to urban areas. Forest structure rapidly recovers through secondary succession, reaching mature forest levels of local biodiversity and biomass in approximately 40 years. Despite the rapid structural recovery, the legacy of pre-abandonment land use, including widespread abundance of exotic species and broadscale floristic homogenization, is likely to persist for centuries.